Overview

Dataset statistics

Number of variables14
Number of observations161
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.7 KiB
Average record size in memory112.8 B

Variable types

Numeric14

Alerts

Price is highly correlated with Product_id and 12 other fieldsHigh correlation
weight is highly correlated with Price and 6 other fieldsHigh correlation
resoloution is highly correlated with Price and 10 other fieldsHigh correlation
ppi is highly correlated with Price and 10 other fieldsHigh correlation
cpu core is highly correlated with Price and 7 other fieldsHigh correlation
cpu freq is highly correlated with Price and 10 other fieldsHigh correlation
internal mem is highly correlated with Product_id and 9 other fieldsHigh correlation
ram is highly correlated with Product_id and 12 other fieldsHigh correlation
RearCam is highly correlated with Price and 10 other fieldsHigh correlation
Front_Cam is highly correlated with Price and 9 other fieldsHigh correlation
battery is highly correlated with Product_id and 12 other fieldsHigh correlation
thickness is highly correlated with Price and 7 other fieldsHigh correlation
Product_id is highly correlated with Price and 3 other fieldsHigh correlation
Sale is highly correlated with Price and 5 other fieldsHigh correlation
cpu core has 10 (6.2%) zeros Zeros
cpu freq has 10 (6.2%) zeros Zeros
internal mem has 10 (6.2%) zeros Zeros
ram has 2 (1.2%) zeros Zeros
RearCam has 8 (5.0%) zeros Zeros
Front_Cam has 42 (26.1%) zeros Zeros

Reproduction

Analysis started2022-10-16 23:32:01.180518
Analysis finished2022-10-16 23:32:10.104775
Duration8.92 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Product_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct83
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean675.5590062
Minimum10
Maximum1339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.143448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile43
Q1237
median774
Q31026
95-th percentile1221
Maximum1339
Range1329
Interquartile range (IQR)789

Descriptive statistics

Standard deviation410.8515828
Coefficient of variation (CV)0.6081653549
Kurtosis-1.318761499
Mean675.5590062
Median Absolute Deviation (MAD)346
Skewness-0.2168693848
Sum108765
Variance168799.0231
MonotonicityNot monotonic
2022-10-16T18:32:10.191861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
662
 
1.2%
10622
 
1.2%
8322
 
1.2%
1332
 
1.2%
8272
 
1.2%
10802
 
1.2%
1832
 
1.2%
11432
 
1.2%
8412
 
1.2%
1312
 
1.2%
Other values (73)141
87.6%
ValueCountFrequency (%)
101
0.6%
142
1.2%
302
1.2%
322
1.2%
401
0.6%
432
1.2%
562
1.2%
642
1.2%
662
1.2%
932
1.2%
ValueCountFrequency (%)
13392
1.2%
13272
1.2%
12962
1.2%
12482
1.2%
12212
1.2%
12162
1.2%
12062
1.2%
11982
1.2%
11612
1.2%
11452
1.2%

Price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2215.596273
Minimum614
Maximum4361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.239553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum614
5-th percentile791
Q11734
median2258
Q32744
95-th percentile3316
Maximum4361
Range3747
Interquartile range (IQR)1010

Descriptive statistics

Standard deviation768.187171
Coefficient of variation (CV)0.3467180281
Kurtosis-0.05631901638
Mean2215.596273
Median Absolute Deviation (MAD)517
Skewness0.0523467823
Sum356711
Variance590111.5297
MonotonicityNot monotonic
2022-10-16T18:32:10.287090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27444
 
2.5%
17344
 
2.5%
18312
 
1.2%
16762
 
1.2%
8332
 
1.2%
23432
 
1.2%
15222
 
1.2%
25622
 
1.2%
15112
 
1.2%
18102
 
1.2%
Other values (71)137
85.1%
ValueCountFrequency (%)
6142
1.2%
6282
1.2%
7052
1.2%
7542
1.2%
7912
1.2%
8332
1.2%
12382
1.2%
13022
1.2%
13152
1.2%
13472
1.2%
ValueCountFrequency (%)
43612
1.2%
38372
1.2%
36582
1.2%
35512
1.2%
33162
1.2%
32872
1.2%
32601
0.6%
32112
1.2%
31162
1.2%
31022
1.2%

Sale
Real number (ℝ≥0)

HIGH CORRELATION

Distinct125
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean621.4658385
Minimum10
Maximum9807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.339476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q137
median106
Q3382
95-th percentile3248
Maximum9807
Range9797
Interquartile range (IQR)345

Descriptive statistics

Standard deviation1546.618517
Coefficient of variation (CV)2.488662162
Kurtosis19.73444674
Mean621.4658385
Median Absolute Deviation (MAD)87
Skewness4.269384098
Sum100056
Variance2392028.838
MonotonicityIncreasing
2022-10-16T18:32:10.397217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165
 
3.1%
404
 
2.5%
264
 
2.5%
103
 
1.9%
573
 
1.9%
453
 
1.9%
433
 
1.9%
243
 
1.9%
112
 
1.2%
1012
 
1.2%
Other values (115)129
80.1%
ValueCountFrequency (%)
103
1.9%
112
 
1.2%
121
 
0.6%
132
 
1.2%
141
 
0.6%
151
 
0.6%
165
3.1%
172
 
1.2%
192
 
1.2%
201
 
0.6%
ValueCountFrequency (%)
98071
0.6%
89461
0.6%
88091
0.6%
80161
0.6%
46381
0.6%
44081
0.6%
36191
0.6%
32911
0.6%
32481
0.6%
21731
0.6%

weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.426087
Minimum66
Maximum753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.448130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile97
Q1134.1
median153
Q3170
95-th percentile310
Maximum753
Range687
Interquartile range (IQR)35.9

Descriptive statistics

Standard deviation92.88861241
Coefficient of variation (CV)0.5450375237
Kurtosis19.96365386
Mean170.426087
Median Absolute Deviation (MAD)18
Skewness3.98175606
Sum27438.6
Variance8628.294315
MonotonicityNot monotonic
2022-10-16T18:32:10.496110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1507
 
4.3%
1586
 
3.7%
1456
 
3.7%
1546
 
3.7%
1746
 
3.7%
1686
 
3.7%
1604
 
2.5%
1704
 
2.5%
1694
 
2.5%
1474
 
2.5%
Other values (52)108
67.1%
ValueCountFrequency (%)
662
1.2%
69.82
1.2%
77.92
1.2%
78.42
1.2%
974
2.5%
102.92
1.2%
1103
1.9%
1122
1.2%
1162
1.2%
1183
1.9%
ValueCountFrequency (%)
7532
1.2%
4892
1.2%
4042
1.2%
3932
1.2%
3102
1.2%
2792
1.2%
2602
1.2%
2382
1.2%
2022
1.2%
194.82
1.2%

resoloution
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.209937888
Minimum1.4
Maximum12.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.545454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile2.4
Q14.8
median5.15
Q35.5
95-th percentile8
Maximum12.2
Range10.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.50995302
Coefficient of variation (CV)0.2898216931
Kurtosis6.595614499
Mean5.209937888
Median Absolute Deviation (MAD)0.35
Skewness1.17611012
Sum838.8
Variance2.279958121
MonotonicityNot monotonic
2022-10-16T18:32:10.650024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5.531
19.3%
530
18.6%
5.714
 
8.7%
412
 
7.5%
5.18
 
5.0%
4.58
 
5.0%
5.27
 
4.3%
66
 
3.7%
86
 
3.7%
74
 
2.5%
Other values (14)35
21.7%
ValueCountFrequency (%)
1.42
 
1.2%
1.52
 
1.2%
2.24
 
2.5%
2.44
 
2.5%
412
 
7.5%
4.58
 
5.0%
4.662
 
1.2%
4.73
 
1.9%
4.84
 
2.5%
530
18.6%
ValueCountFrequency (%)
12.22
 
1.2%
10.12
 
1.2%
86
 
3.7%
74
 
2.5%
66
 
3.7%
5.714
8.7%
5.62
 
1.2%
5.531
19.3%
5.462
 
1.2%
5.432
 
1.2%

ppi
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean335.0559006
Minimum121
Maximum806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.691981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile160
Q1233
median294
Q3428
95-th percentile534
Maximum806
Range685
Interquartile range (IQR)195

Descriptive statistics

Standard deviation134.8266595
Coefficient of variation (CV)0.4024004927
Kurtosis0.2822897588
Mean335.0559006
Median Absolute Deviation (MAD)107
Skewness0.6025028093
Sum53944
Variance18178.22811
MonotonicityNot monotonic
2022-10-16T18:32:10.738305image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
29419
 
11.8%
40115
 
9.3%
5348
 
5.0%
2338
 
5.0%
2456
 
3.7%
4326
 
3.7%
5156
 
3.7%
4284
 
2.5%
1284
 
2.5%
3674
 
2.5%
Other values (35)81
50.3%
ValueCountFrequency (%)
1212
1.2%
1284
2.5%
1292
1.2%
1602
1.2%
1662
1.2%
1672
1.2%
1704
2.5%
1782
1.2%
1842
1.2%
1871
 
0.6%
ValueCountFrequency (%)
8062
 
1.2%
5772
 
1.2%
5412
 
1.2%
5382
 
1.2%
5348
5.0%
5242
 
1.2%
5156
3.7%
5132
 
1.2%
4692
 
1.2%
4414
2.5%

cpu core
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.857142857
Minimum0
Maximum8
Zeros10
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.779454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median4
Q38
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.444016016
Coefficient of variation (CV)0.503179768
Kurtosis-0.8634294489
Mean4.857142857
Median Absolute Deviation (MAD)0
Skewness-0.009077116453
Sum782
Variance5.973214286
MonotonicityNot monotonic
2022-10-16T18:32:10.811844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
481
50.3%
852
32.3%
214
 
8.7%
010
 
6.2%
62
 
1.2%
12
 
1.2%
ValueCountFrequency (%)
010
 
6.2%
12
 
1.2%
214
 
8.7%
481
50.3%
62
 
1.2%
852
32.3%
ValueCountFrequency (%)
852
32.3%
62
 
1.2%
481
50.3%
214
 
8.7%
12
 
1.2%
010
 
6.2%

cpu freq
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.502832298
Minimum0
Maximum2.7
Zeros10
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.847702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.2
median1.4
Q31.875
95-th percentile2.5
Maximum2.7
Range2.7
Interquartile range (IQR)0.675

Descriptive statistics

Standard deviation0.5997827027
Coefficient of variation (CV)0.3991015521
Kurtosis0.8761121428
Mean1.502832298
Median Absolute Deviation (MAD)0.2
Skewness-0.5126630811
Sum241.956
Variance0.3597392905
MonotonicityNot monotonic
2022-10-16T18:32:10.887243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1.235
21.7%
1.320
12.4%
1.87512
 
7.5%
010
 
6.2%
2.38
 
5.0%
1.58
 
5.0%
2.56
 
3.7%
1.46
 
3.7%
1.355
 
3.1%
1.64
 
2.5%
Other values (18)47
29.2%
ValueCountFrequency (%)
010
 
6.2%
0.2082
 
1.2%
14
 
2.5%
1.235
21.7%
1.252
 
1.2%
1.320
12.4%
1.355
 
3.1%
1.46
 
3.7%
1.58
 
5.0%
1.532
 
1.2%
ValueCountFrequency (%)
2.74
2.5%
2.56
3.7%
2.452
 
1.2%
2.38
5.0%
2.262
 
1.2%
2.21
 
0.6%
2.152
 
1.2%
2.12
 
1.2%
24
2.5%
1.9752
 
1.2%

internal mem
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.50171429
Minimum0
Maximum128
Zeros10
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.924406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median16
Q332
95-th percentile64
Maximum128
Range128
Interquartile range (IQR)24

Descriptive statistics

Standard deviation28.80477276
Coefficient of variation (CV)1.175622751
Kurtosis5.954205698
Mean24.50171429
Median Absolute Deviation (MAD)12
Skewness2.389682587
Sum3944.776
Variance829.7149337
MonotonicityNot monotonic
2022-10-16T18:32:10.959500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1640
24.8%
3240
24.8%
828
17.4%
418
11.2%
6411
 
6.8%
010
 
6.2%
1288
 
5.0%
0.0042
 
1.2%
0.1282
 
1.2%
0.2562
 
1.2%
ValueCountFrequency (%)
010
 
6.2%
0.0042
 
1.2%
0.1282
 
1.2%
0.2562
 
1.2%
418
11.2%
828
17.4%
1640
24.8%
3240
24.8%
6411
 
6.8%
1288
 
5.0%
ValueCountFrequency (%)
1288
 
5.0%
6411
 
6.8%
3240
24.8%
1640
24.8%
828
17.4%
418
11.2%
0.2562
 
1.2%
0.1282
 
1.2%
0.0042
 
1.2%
010
 
6.2%

ram
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.204993789
Minimum0
Maximum6
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:10.994173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008
Q11
median2
Q33
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.609831422
Coefficient of variation (CV)0.730084334
Kurtosis0.04436743548
Mean2.204993789
Median Absolute Deviation (MAD)1
Skewness0.7926980074
Sum355.004
Variance2.591557206
MonotonicityNot monotonic
2022-10-16T18:32:11.026311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
136
22.4%
234
21.1%
327
16.8%
421
13.0%
0.51212
 
7.5%
612
 
7.5%
0.0046
 
3.7%
1.55
 
3.1%
0.0082
 
1.2%
0.0322
 
1.2%
Other values (2)4
 
2.5%
ValueCountFrequency (%)
02
 
1.2%
0.0046
 
3.7%
0.0082
 
1.2%
0.0322
 
1.2%
0.1282
 
1.2%
0.51212
 
7.5%
136
22.4%
1.55
 
3.1%
234
21.1%
327
16.8%
ValueCountFrequency (%)
612
 
7.5%
421
13.0%
327
16.8%
234
21.1%
1.55
 
3.1%
136
22.4%
0.51212
 
7.5%
0.1282
 
1.2%
0.0322
 
1.2%
0.0082
 
1.2%

RearCam
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.37826087
Minimum0
Maximum23
Zeros8
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:11.060482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3
Q15
median12
Q316
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.181585008
Coefficient of variation (CV)0.5956282161
Kurtosis-0.9605745072
Mean10.37826087
Median Absolute Deviation (MAD)4
Skewness0.1069292938
Sum1670.9
Variance38.21199321
MonotonicityNot monotonic
2022-10-16T18:32:11.093673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1327
16.8%
1624
14.9%
823
14.3%
522
13.7%
1212
7.5%
210
 
6.2%
08
 
5.0%
3.156
 
3.7%
216
 
3.7%
205
 
3.1%
Other values (8)18
11.2%
ValueCountFrequency (%)
08
 
5.0%
1.32
 
1.2%
210
6.2%
32
 
1.2%
3.156
 
3.7%
42
 
1.2%
522
13.7%
823
14.3%
102
 
1.2%
1212
7.5%
ValueCountFrequency (%)
232
 
1.2%
21.52
 
1.2%
216
 
3.7%
20.74
 
2.5%
205
 
3.1%
1624
14.9%
1327
16.8%
12.32
 
1.2%
1212
7.5%
102
 
1.2%

Front_Cam
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.50310559
Minimum0
Maximum20
Zeros42
Zeros (%)26.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:11.128011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q38
95-th percentile16
Maximum20
Range20
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.342053408
Coefficient of variation (CV)0.9642353085
Kurtosis1.355740292
Mean4.50310559
Median Absolute Deviation (MAD)3
Skewness1.169749541
Sum725
Variance18.8534278
MonotonicityNot monotonic
2022-10-16T18:32:11.161338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
042
26.1%
542
26.1%
829
18.0%
215
 
9.3%
168
 
5.0%
1.24
 
2.5%
5.14
 
2.5%
134
 
2.5%
3.72
 
1.2%
42
 
1.2%
Other values (5)9
 
5.6%
ValueCountFrequency (%)
042
26.1%
0.92
 
1.2%
12
 
1.2%
1.24
 
2.5%
215
 
9.3%
2.12
 
1.2%
2.22
 
1.2%
3.72
 
1.2%
42
 
1.2%
542
26.1%
ValueCountFrequency (%)
201
 
0.6%
168
 
5.0%
134
 
2.5%
829
18.0%
5.14
 
2.5%
542
26.1%
42
 
1.2%
3.72
 
1.2%
2.22
 
1.2%
2.12
 
1.2%

battery
Real number (ℝ≥0)

HIGH CORRELATION

Distinct55
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2842.111801
Minimum800
Maximum9500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:11.203678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum800
5-th percentile950
Q12040
median2800
Q33240
95-th percentile4500
Maximum9500
Range8700
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation1366.990838
Coefficient of variation (CV)0.4809771512
Kurtosis7.570727849
Mean2842.111801
Median Absolute Deviation (MAD)600
Skewness2.092659847
Sum457580
Variance1868663.95
MonotonicityNot monotonic
2022-10-16T18:32:11.244857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300013
 
8.1%
25008
 
5.0%
32006
 
3.7%
40006
 
3.7%
23006
 
3.7%
20005
 
3.1%
21004
 
2.5%
22004
 
2.5%
27004
 
2.5%
35004
 
2.5%
Other values (45)101
62.7%
ValueCountFrequency (%)
8004
2.5%
8502
1.2%
9002
1.2%
9502
1.2%
11002
1.2%
12002
1.2%
14002
1.2%
15002
1.2%
15602
1.2%
16002
1.2%
ValueCountFrequency (%)
95002
 
1.2%
74002
 
1.2%
70002
 
1.2%
50002
 
1.2%
45002
 
1.2%
44502
 
1.2%
40802
 
1.2%
40602
 
1.2%
40006
3.7%
36302
 
1.2%

thickness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct49
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.92173913
Minimum5.1
Maximum18.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2022-10-16T18:32:11.298335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile6.3
Q17.6
median8.4
Q39.8
95-th percentile12.9
Maximum18.5
Range13.4
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation2.192564014
Coefficient of variation (CV)0.2457552258
Kurtosis4.298591437
Mean8.92173913
Median Absolute Deviation (MAD)0.9
Skewness1.587679908
Sum1436.4
Variance4.807336957
MonotonicityNot monotonic
2022-10-16T18:32:11.352805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
8.510
 
6.2%
7.69
 
5.6%
88
 
5.0%
7.86
 
3.7%
8.46
 
3.7%
7.36
 
3.7%
5.16
 
3.7%
7.96
 
3.7%
7.45
 
3.1%
10.94
 
2.5%
Other values (39)95
59.0%
ValueCountFrequency (%)
5.16
3.7%
5.92
 
1.2%
6.32
 
1.2%
6.41
 
0.6%
6.81
 
0.6%
6.92
 
1.2%
74
2.5%
7.36
3.7%
7.45
3.1%
7.54
2.5%
ValueCountFrequency (%)
18.52
1.2%
15.62
1.2%
14.12
1.2%
13.22
1.2%
12.92
1.2%
12.42
1.2%
12.32
1.2%
11.72
1.2%
11.62
1.2%
112
1.2%

Interactions

2022-10-16T18:32:09.336596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.100807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.841000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.441737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.924929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.488599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.968268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.532763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.102285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.582170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.138921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.627771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.186546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.830906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.374724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.257928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.880249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.476764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.962403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.524256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.006677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.568895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.137628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.617578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.175557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.662152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.229639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.869082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.414320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.374940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.920672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.513007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.999944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.562721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.108597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.607736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.174486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.655242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.212909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.698057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.270598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.906496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.451351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.410962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.956085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.546389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.034986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.596093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.142860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.641529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.207471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.689000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.246597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.799400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.312645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.940447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.490029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.450814image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.994395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.580184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.070900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.631554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.177702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.676971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.241690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.723967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.281332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.833958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.358410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.977336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.541218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.490411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.033034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.613809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.105005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.664185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.212358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.712415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.273885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.759731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.314637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.867089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.398755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.014269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.581790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.531890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.072577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.650591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.206681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.698865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.248909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.749415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.308507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.794268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.349448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.902195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.444549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.052001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.619410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.573194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.112503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.686070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.243241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.734206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.285854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.786370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.344632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.896694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.386444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.937793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.486784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.090167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.652589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.610643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.149421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.719642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.277565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.768318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.320441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.822231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.379369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.932947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.420043image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.971859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.527456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.126186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.686589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.650519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.187414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.754002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.313819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.801715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.354855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.858832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.414707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.967714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.456264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.005739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.567186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.165210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.788649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.688582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.292040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.787737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.349499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.834994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.390837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.894833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.449497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.002335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.491013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.040111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.606798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.202609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.823881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.723393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.328587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.820582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.384038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.867031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.425706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.996703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.480726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.035889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.523773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.074699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.642063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.234057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.868454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.767908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.369923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.859944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.422125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.903878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.463851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.034830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.517502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.072964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.561091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.115354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.683598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.271149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.903206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:02.803164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.404174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:03.890532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.453543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:04.934275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:05.495905image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.066788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:06.549155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.103788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:07.591970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.148306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:08.785743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-16T18:32:09.301978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-16T18:32:11.476033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-16T18:32:11.554038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-16T18:32:11.637489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-16T18:32:11.713856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-16T18:32:09.970563image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-16T18:32:10.063138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Product_idPriceSaleweightresoloutionppicpu corecpu freqinternal memramRearCamFront_Cambatterythickness
0203235710135.05.242481.3516.03.00013.008.026107.4
1880174910125.04.023321.304.01.0003.150.017009.9
240191610110.04.731241.208.01.50013.005.020007.6
399131511118.54.023321.304.00.5123.150.0140011.0
4880174911125.04.023321.304.01.0003.150.017009.9
5947213712150.05.540142.3016.02.00016.008.025009.5
6774123813134.14.023321.208.01.0002.000.0156011.7
7947213713150.05.540142.3016.02.00016.008.025009.5
899131514118.54.023321.304.00.5123.150.0140011.0
91103258015145.05.143242.5016.02.00016.002.028008.1

Last rows

Product_idPriceSaleweightresoloutionppicpu corecpu freqinternal memramRearCamFront_Cambatterythickness
15185130552173158.05.5040141.87564.06.016.08.030007.4
15229043613248238.05.7051581.950128.06.012.08.070007.4
15329043613291238.05.7051581.950128.06.012.08.070007.4
154113125363619202.06.0036781.50016.03.021.516.027008.4
155120635514408178.05.4653841.875128.06.012.016.040808.4
156120635514638178.05.4653841.875128.06.012.016.040808.4
157129632118016170.05.5053441.975128.06.020.08.034007.9
15885632608809150.05.5040182.20064.04.020.020.030006.8
159129632118946170.05.5053441.975128.06.020.08.034007.9
160113125369807202.06.0036781.50016.03.021.516.027008.4